TruSeq technology represents advancements in Illumina sequencing, aimed at optimizing data accuracy, research scalability, and the user experience. Illumina sequencing systems deliver highly accurate data across a broad range of applications.
A typical sequencing workflow comprises sample/library preparation, cluster amplification, DNA sequencing, image analysis/base calling, read alignment, and variant discovery. If any of these steps generate poor results, the quality of the final data set is compromised. With TruSeq technology, each step in this process is optimized to deliver accurate data to ensure the highest standard of quality for any research project.
Platform accuracy describes the overall accuracy of the sequencing workflow, accounting for each step of the process, from sample preparation through variant discovery. It ultimately determines the reliability of a sequencing experiment. The sequencing workflow can be segmented into three main stages that each provide a unique accuracy contribution: Sample/Library Accuracy, Detection Accuracy, and Algorithm Accuracy.
Sample accuracy is associated with the library preparation stage of the sequencing workflow. In this stage, DNA is fragmented in preparation for library construction.
Each fragment in the library will eventually correspond to a sequencing read, so high fragment size uniformity and library diversity is important for achieving even coverage across the genome. Errors that occur during library preparation, such as missing fragments due to a non-diverse library, cannot be identified by the sequencer.
The portions of the genome not represented in the library will not be sequenced, leading to gaps in the data set. These gaps cannot be corrected for by error correction methods employed by some sequencing technologies.
Hence, quality scores do not reflect errors introduced during library preparation, as the sequencing signal will appear clean and error-free. The maximal achievable accuracy of most sequencing platforms is limited by the sample accuracy.
Detection accuracy accounts for the second stage of the sequencing workflow, comprising cluster generation, DNA sequencing, and primary data analysis. Any errors that occur during this stage are typically reflected in the quality scores.
Quoted error rates for sequencing systems are usually dominated by detection accuracy.
Detection errors are less harmful than sample errors because they can be tracked using the well-established per-base quality scores. Conversely, sample errors cannot be tracked directly, but manifest themselves by lowering the overall system accuracy.
Detection errors can be improved by re-sequencing, single-read error correction, or encoding schemes.Learn More About Quality Scores
Algorithm accuracy pertains to the secondary data analysis phase of the workflow, typically involving alignment and variant calling. The accuracy of the alignment method is critical.
Regardless of how high the quality of data is from the sequencing instrument, sub-optimal alignment will lead to a poor final data set, potentially with incorrectly placed mismatches, non-uniform coverage, and a high number of gaps.
In turn, this can lead to high false positive and false negative rates. The variant calling method, by itself, also needs to be highly accurate for the same reasons.
Illumina offers user-friendly bioinformatics tools that enable researchers to perform accurate alignment and variant calling.Explore Bioinformatics Tools
Simple, all-inclusive whole-genome sequencing (WGS) library preparation that provides accurate and comprehensive coverage of complex genomes.
Generate whole-genome sequencing libraries and efficiently interrogate samples with limited available DNA.
Prepare sequencing libraries from mRNA to get a clear view of the coding transcriptome with strand-specific information.